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Bress/etal/2016a: Robust Query Processing in Co-Processor-Accelerated Databases

Bibtype Inproceedings
Bibkey Bress/etal/2016a
Author Sebastian Breß and Henning Funke and Jens Teubner
Title Robust Query Processing in Co-Processor-Accelerated Databases
Booktitle Proceedings of the 2016 ACM SIGMOD Conference on Management of Data
Address San Francisco, CA, USA
Publisher ACM
Abstract Technology limitations are making the use of heterogeneous computing
devices much more than an academic curiosity. In fact, the use of such
devices is widely acknowledged to be the only promising way to achieve
application-speedups that users urgently need and expect. However,
building a robust and efficient query engine for heterogeneous
co-processor environments is still a significant challenge.

In this paper, we identify two effects that limit performance in case
co-processor resources become scarce. Cache thrashing occurs when the
working set of queries does not fit into the co-processor's data cache,
resulting in performance degradations up to a factor of 24. Heap
contention occurs when multiple operators run in parallel on a
co-processor and when their accumulated memory footprint exceeds the
main memory capacity of the co-processor, slowing down query execution
by up to a factor of six.

We propose solutions for both effects. Data-driven operator placement
avoids data movements when they might be harmful; query chopping limits
co-processor memory usage and thus avoids contention. The combined
approach-data-driven query chopping-achieves robust and scalable
performance on co-processors. We validate our proposal with our
open-source GPU-accelerated database engine CoGaDB and the popular star
schema and TPC-H benchmarks.
Month 06
Year 2016
Projekt SFB876-C5
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